e-estonia assessment of a digital eform · citizens: id-number, name, birth, gender, address...
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E-ESTONIA: ASSESSMENT OF A DIGITAL REFORM
By
Gergő BATIZ
Submitted to Central European University Department of Economics
In partial fulfillment of the requirements for the degree of Master of Economic Policy in Global Markets
Supervisor: Professor Lajos BOKROS
Budapest, Hungary
2016
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EXECUTIVE SUMMARY
This thesis studies the association between the usage intensity of digital services
and 1) citizens’ attitudes, 2) competitiveness and productivity, and 3) openness in the
case of Estonia.
Citizens’ attitudes are measured through their trust in different institutions,
through perceived levels of corruption, and through election turnouts. The limited data
available fails to confirm any association between usage and trust levels, and between
usage and corruption perception, but identifies a higher election turnout rate when the
e-voting service is available.
Competitiveness and productivity is measured using the two most popular
rankings from World Bank and World Economic Forum, and labor and total factor
productivity data. No association shows up related to productivity data, but a higher
increase in usage levels are associated by a less deteriorating score of regulatory
burden, confirming a favorable connection between service usage and business
environment.
Openness is measured by FDI and business demographics. The analysis
uncovers a positive relationship between usage intensity and FDI stocks but no visible
change in company ownership patterns.
Based on the results, further research is recommended to the scientific
community, and the continuation and extension of reforms to Estonian policymakers.
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TABLE OF CONTENTS
CONTENTS Executive Summary ............................................................................................. i
Table of Contents ................................................................................................ ii
Introduction ........................................................................................................ 1
Importance of the Topic and Aim of Research ................................................ 1
E-Government Services in Estonia ................................................................. 2
What is E-Government? .............................................................................. 2
Infrastructure ............................................................................................... 3
Principles .................................................................................................... 5
Legal framework, Establishment of System, Keys to Success .................... 6
Literature Review ............................................................................................... 9
E-Government and Citizens' Trust in the State ............................................... 9
What is Trust? ............................................................................................. 9
Why is Trust Important? ............................................................................ 10
What Drives Trust? ................................................................................... 11
E-Government, Competitiveness and Productivity ....................................... 14
Competitiveness and Administrative Burden ............................................. 14
What Government can do about it? .......................................................... 16
E-Government and the Openness of the Country ......................................... 17
Why is Openness Important? .................................................................... 17
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How can E-Government Influence Openness? ......................................... 19
Analysis ............................................................................................................ 20
Usage Data ................................................................................................... 20
Citizens’ Attitudes ......................................................................................... 21
Hypotheses ............................................................................................... 21
Data Sources and Description .................................................................. 22
Trust in Institutions .................................................................................... 25
Corruption ................................................................................................. 27
Election Turnout ........................................................................................ 28
Competitiveness and Productivity ................................................................. 30
Hypotheses ............................................................................................... 30
Data Sources and Description .................................................................. 31
Competitveness ........................................... Error! Bookmark not defined.
Productivity ............................................................................................... 34
Openness ..................................................................................................... 34
Hypotheses ............................................................................................... 34
Data Sources and Description .................................................................. 35
FDI ............................................................................................................ 36
Business Demography .............................................................................. 36
Conclusions ...................................................................................................... 38
Policy Recommendations ................................................................................. 40
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References ....................................................................................................... 42
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INTRODUCTION
Importance of the Topic and Aim of Research
We are living in an age of where everyone and everything are connected. Yet in
all too many countries we do official business as if we were still living in the 20th or
19th century: endless paperwork, spending long hours waiting in government offices,
going back and forth between administrators.
E-government is one of the buzzwords when it comes to public administration
today. Indeed, properly implementing electronic services can result in enormous cost
savings for the state, the private sector and ordinary citizens alike, while a lean
administrative environment can also increase the competitiveness and attractiveness
of a country.
Estonia is currently the go-to country when it comes to the best e-government
implementation, at least according to popular media, and implementing the Estonian
model in another country seems to promise the same benefits.
But is the system really so great? There is little scientific work regarding macro-
level changes in association with e-government service usage in Estonia.
Therefore, the aim of this graduation thesis is to study the association between
the usage of e-government services in Estonia and changes in three main areas:
Attitudes towards the state, competitiveness and productivity, and openness of the
economy.
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E-Government Services in Estonia
What is E-Government?
Van Dijk (2012) defines electronic government (e-government) as follows: “all
data, communication and transaction processing activities related to governmental
tasks and responsibilities in which ICT is being used”. This is no doubt a loose definition
as according to it, any involvement of IT already makes for having
e-government in a country. However, it is obvious that it is wrong to equate a system
where electronic tax filing takes two hours and it is one of a dozen services available
online, with an other system where tax filing takes 5 minutes and hundreds of services
are available. Therefore, when a government claims that it has implemented e-
government, this practically doesn't mean anything until one looks more deeply into
the scope of services available and the quality of their implementation.
The reason why the Estonian e-government system is the topic of this paper is
that it offers services in unparalleled number and quality, and it have effectively
transformed the way how citizens interact with government, and how people transact
and get things done in general. As an example: Automated tax-declarations, no need
for driver’s license and registration documents while driving, electronic voting even on
national elections, or a digital signature that is today preferred to manual signatures,
just to name a very few from the many hundreds of municipal and state services
Estonians can use today. (eesti.ee 2016)
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Figure 1 – Prime Minister Taavi Roivas authenticating via his smartphone before
approving three bills from Parliament (Source: The Huffington Post online)
Infrastructure
The Estonian system is built on three core elements: the ID-cards, the National
Register, and the so-called X-Road network.
The ID-card is compulsory to all Estonian citizens. It is both a traditional ID with
a biometric data chip, but also a key to all digital activities: It contains a cryptographic
key-pair that is used to identify a citizen and to encrypt communications between the
user's computer and the servers of the system. Public-key cryptography is also used
to generate the time-stamped digital signatures on any document or transaction. The
ID-cards have a SIM-card extension as well, so users don't need to use a USB card
reader anymore but they can do all authentication via their cellphones if they prefer
(European Commission 2016).
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The National Register is the database containing the basic information of all
citizens: ID-number, name, birth, gender, address history, legal status / citizenship,
and legal relationships. In all other databases, only the IDs are used, so this is the
database that is needed to observe real the identity of citizens, other databases by
themselves are anonymous.
Figure 2 - The X-Road infrastructure connecting all elements of the system.
(Source: Republic of Estonia Information System Authority (RIA) 2016)
X-Road is a secure data-sharing network connecting all users and servers of the
e-government system using standard data formats.
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Principles
The general organizing principle is that no data should be stored in more than
one place. Different national databases (healthcare, education, criminal records, etc.)
exist separately and only IDs of citizens are stored, but not their identity (which must
be queried from the National Register based on ID in case it is needed). This way data
on people is always stored separately and anonymously and can only be connected
with the authorization of the data owner (the citizen) or in well-defined cases, resulting
in fair privacy. Furthermore, all citizens and entities can access all the data stored on
them and also view a full log of who requested data on them (e.g.: doctors and
pharmacists query medical information, police query criminal record and driver’s
license in case of a routine roadside check, educational institutions query data when
processing applications, etc.). Unauthorized query is a criminal offense punishable
even with imprisonment. Since it is in the mutual interest of all parties to maintain trust
in the system, regulations are strictly enforced and independent security audits are
frequently taking place (Herlihy 2013).
Some entities are also linked together. This way board members and employees
are linked to companies so they can securely transact in the name of their companies
as well. Property is also linked to entities, this way owners of land and real estate can
be seen when browsing the land registry, just like the wealth and business stakes of
public officials. This radically improves transparency (Herlihy 2013).
Some third parties can also connect to these databases. All banks, utility and
telecommunications companies are connected to the system, so banks can
autonomously perform credit assessment, telecommunication companies can check
regular income before contracting for a subscription, etc., provided that the client
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granted access to his / her data to them. The system makes anonymized data available
by offering API-s and providing built in visualization tools via the main website, enabling
researchers and statisticians to work more efficiently (Herlihy 2013).
Legal framework, Establishment of System, Keys to Success
There is no one comprehensive
legislation governing e-government services.
Instead, numerous laws regulate different
aspects of the service, such as the data
protection act, signatures act, consumer
protection act, archives act, public information
act, and so on. The system itself was
established in a gradual way, overarching
different governments (Herlihy 2013; Vassil
2015).
1998 -2003
•Principles of the Estonian Information Policy I
•Principles of the Estonian Information Policy II
2004-2006
•Estonian Information society
2007-2013
•Programme for Increasing Awareness of the Information Society
2008-2011
•Information Society Strategy for Local Governments
2009-2011
•Information Security Policy
•Implementation Plan
2011•Estonian Broadband Strategy
2012•Estonian Cyber Security Strategy
2013•Estonian Information Society Startegy
2014-2017
•Cyber Security Strategy
2014-2020
•Estonian Information Society Strategy
Figure 3 – Legislative Timeline Leading up
to the Current System
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New ID-cards were
introduced in 2002, but as it can
be seen from the chart, usage
growth wasn't significant until
around 2006. The main reason
for this was a digital divide in
society, and that many
households did not realize the
benefits of having an internet
subscription and therefore using
the services provided by the
system. The solution for this
fundamental obstacle came
primarily from the private sector:
Banks, utilities and telecommunications companies wanted to go as digital as possible
for cost-efficiency reasons, and with the backing of the government, there was a joint
effort launched (‘Tiger Leap Project’) aimed at educating the population on ICT and
introducing digital solutions in public schools. Since companies actively pushed for
using the new-generation ID-cards, people were getting them anyway and they stared
to realize the benefits. This is when service usage growth picked up enormous
momentum and made today's digital society in Estonia a reality (Vassil 2015).
All-in-all, the most important enablers of realizing this system were probably the
strong cooperation between the government and the private sector in transforming the
way Estonians transact; and a political consensus overarching all governments that
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Mill
ion
sCumulative number of
Authentications and Signatures (annual average)
Authentications Signatures
Figure 4 – Cumulative Number of
Authentications and Signatures
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the country should specialize on ICT after the change of systems, and probably also
some sort cultural setup that enabled citizens embrace this new way of doing things.
For the rest of this thesis, the term ‘digital services’ will be used to refer to services
that can be used via the e-government system.
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LITERATURE REVIEW
E-Government and Citizens' Trust in the State
What is Trust?
Literature primarily on trust itself goes back to the second half of the 20st century.
Arrow (1974) describes trust as a lubricant to economic exchange and an efficient
mechanic governing transactions. He thinks of trust as a form of 'implicit contracting'
where parties agree not to unfairly exploit the other and suggests that trust is a unique
intangible commodity. Such view of trust as an asset gets carried forward by other
scholars later: Coleman (1990), Putnam, Leonardi, and Nanetti (1993) and Fukuyama
(1995) all include trust as a component to 'social capital', together with norms and
networks. Putnam also argues that such social capital enhances a society's ability to
facilitate coordinated action, that is, to organize. Levi (1996) extends the concept with
a risk factor: She describes trust as a set of socio-psychological phenomena that
enable individuals to take risk when engaging with others, solve problems of collective
action, and mutually act against their narrow short-term self-interests, in order to reap
benefits from cooperation. Levi further suggests that trust is formed by a rational
(although possibly subconscious) decision.
Partly due to its intangible nature, the concept of trust is not straightforward to
grasp. It can be concluded however, that it is an intangible social asset, it influences
interactions among individuals and how they organize as a group, and it serves as one
end of an implicit agreement to put aside selfish short-run self-interest for longer-term
cooperation and mutual benefit.
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Why is Trust Important?
As the previous paragraph already suggests, trust has far reaching implications
for society. Leibenstein (1987) have studied trust as an economic input from a game
theory perspective, and concluded that it served as a coordinating mechanism
enabling players to play out games to the highest individual and social benefit.
Continuing Leibenstein's line of thought, we can think of (economic) life as an infinitely
repeated prisoner's dilemma, where players who trust each other can play out the
socially optimal outcome. Using this example, it can be argued that trust on a social
level is no else than people playing nice with others in anticipation of being
reciprocated. This implicit agreement makes production and exchange possible
(Misztal 1996). Therefore, societies with higher levels of trust and social capital in
general are more likely to produce the set of institutions necessary for economic
growth, than societies with less of it (Levi 1996). As Levi (1996) continues to elaborate,
trust has serious consequences to government as well: Trust affects citizens' tolerance
level of the regime and their compliance with the its laws.
Putnam, Leonardi, and Nanetti (1993) had compared Northwestern and Southern
Italian societies and they had concluded that the better performance of institutions in
the North was attributed to higher levels of trust in that society, as the dominant
interpersonal relation was generalized towards the government and the individual too.
Based on their findings, they establish that a high level of trust is essential to well-
functioning institutions. Guiso, Sapienza, and Zingales (2000) had further investigated
the issue, but focusing on the financial profile of households. They have found that in
areas with high levels of social trust, households are less risk-averse: they invest a
larger portion of their savings into stocks, they tend to use more of formal arrangements
for getting credit and less informal lending. Perhaps not surprisingly, firms in these
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areas had easier access to credit and more shareholders on average, overall leading
to a more financially sound economy. Continuing on the fiscal line of argument, it is
important to point out the work of Randlane (2012) on tax compliance in Estonia: He
confirmed the seemingly trivial claim that general attitudes towards the state affect
compliance with the obligation to pay taxes, as the more people trust the state, the less
tax they avoid. Lühiste (2006) also point out how higher levels of social trust are leading
to more equal societies through solidarity.
This section about the importance of trust is probably summarized the best by
Bjornskov and Meon (2013), who conducted a sophisticated inquiry into the casual
mechanism from trust to economic growth. They have uncovered a robust chain of
causality: Higher levels of trust lead to better education, legal and bureaucratic
institutions, and these ensure a significantly higher per capita growth of income. In their
analysis, the quality of political institutions doesn't play a significant role in this chain,
only economic-judicial institutions.
What Drives Trust?
As Mishler and Rose (1998) summarize, there are two main approaches when it
comes to what influences trust in institutions:
Cultural theorists claim that trust in institutions is politically exogenous and
national political trust is directly proportional to the prevailing national interpersonal
trust. Many of the already showcased ideas fall into this category, such as Fukuyama’s
(1995) claim that more trusting individuals are more likely to organize, or Putnam,
Leonardi, and Nanetti (1993) and Helliwell and Putnam (1996), claiming that
institutional trust is a bottom-up construct stemming from the personal interactions
between people.
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Institutional theorists, on the other hand, assert that trust in institutions is
politically endogenous and is a function of the perceived policy performance and ability
of the government to deliver economic prosperity (Przeworski et al. 1996).
As it is generally the case, the true nature of trust is likely to be correctly described
by both of these approaches. Culture should definitely have a strong effect or an initial
calibration on an individual's trust in institutions, but a rational individual is also rightfully
expected to reevaluate his trust in institutions based on their performance. Lühiste
(2006) have examined exactly these relationships in the Baltic states and have
confirmed both mechanics with real data:
The cultural explanation was confirmed by the study, as variation in interpersonal
trust explains 11% of the variation in trust towards government among different nations.
This weak but robust finding is in line with the results of Mishler and Rose (2001) who
also concluded that institutional explanations of trust are stronger that cultural ones.
Institutional theories were also confirmed (R2 = 15%): Governmental and
economic performance of the government (outcomes) influences trust, and the less
corrupt a government is perceived by its citizens, the more trusting they are. Also it is
worth pointing out here that Estonia is the least corrupt country in the CEE-region
according to Transparency International's Corruption Perception Index. Political
performance (how government works) also significantly influences public trust in
institutions. '[…] transparent and efficient administration' is recognized as a driver of
this performance, therefore a well-functioning e-government system can in theory
strengthen trust in the state.
The study had some more general findings as well. In general, Baltic people
distrust their parliaments and parties more (57% of population don't trust) than other
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CEE countries (51%) while having roughly the same level of interpersonal trust in the
society. It is also worth pointing out that the most trusted institution also happens to be
the least democratic one: the military. The author explains the situation by history, and
by the fact that the older population tends to be more suspicious towards government
and the younger ones are expected to bring about change in trust, but the results show
only a weak negative correlation between age and level of trust.
Lühiste (2006) have examined the drivers of trust in a comprehensive framework,
but there are other scholars pointing out some more concrete relationships: Lipsky
(2010) emphasizes how important the role of government's agents is: Citizens tend to
project their experience with street-level bureaucrats on the state in general. Levi
(1996) further underlines the importance of using high standards in the recruitment of
government and administration employees exactly for this reason. Most importantly for
the present study, Im et al. (2014) have analyzed the relationship between trust in the
government and internet and e-government usage: They have concluded that in
general, internet usage is negatively correlated with trust in government (most probably
because it leads to better-informed citizens), but internet usage combined with e-
government usage doesn't exhibit the same relationship. Based on this, it can be
inferred that e-government usage boosts trust in the state, especially in the case of
Estonia, where broadband Internet usage is extremely widespread already (European
Commission 2016), so the trust effects of internet usage are already in the baseline.
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E-Government, Competitiveness and Productivity
Competitiveness and Administrative Burden
As Bjornskov and Meon (2013) proved, trust leads to higher overall productivity
levels and therefore higher growth. Naturally, the level of growth is influenced by many
other factors besides trust. The growth of an economy is the increase of its level of
output. Output is the result of combining input factors using a certain technology. In
case of a country, we can think about technology in the most general sense, also
including everything about how people do business, interact, and live their lives in
general. From a business point of view, this set of cultural factors form an important
determinant of the business environment alongside the regulatory setting, the quality
of institutions, endowments of production factors and capital, and human resources.
Competitiveness is essentially the state of the business environment of an
economy relative to others. Increasing competitiveness is or should be a core objective
of any sane economic policy, as it enables the economy to produce more output using
the same amount of resources, or if the country also competes for investment in an
open setting, it can attract additional inputs to production (FDI, talent etc.) on top of
efficiency gains. Therefore, improving aspects of the business environment is key. A
well-functioning e-government system is favorable in this regard, as by increasing trust,
it creates a culture that is more conductive to cooperation and organization, it increases
the quality of institutions, and it decreases corruption. These are merely indirect effects
through trust, but having a well-functioning e-government system in place also directly
affects the business environment and competitiveness by reducing administrative
burden.
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Allers (1994) defines administrative burden as the cost businesses incur in
complying with data provision and reporting regulations. A. F. M. Nijsen (2003) and
Ntaliani, Costopoulou, and Sideridis (2012) further add that such compliance ties down
considerable resources which is especially burdensome for small and medium sized
enterprises. The importance of reducing this burden is widely recognized by
governments, and countless policies are set up to this end (European Commission
2007; UK Cabinet Office 2006).
Figure 5 - The 12 Pillars of Competitiveness of the
World Economic Forum’s Global Competitiveness Report
(Source: World Economic Forum 2016b)
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What Government can do about it?
As Wegrich (2009) describes, the Standard Cost Model called Mistral is currently
the standard to use by the European Union and the OECD for quantifying
administrative burden. A. F. Nijsen, Vellinga, and others (2001) explain that 3 main
factors influence this hindrance: 1) The amount of regulation and rules to comply with,
2) How control and checks of compliance are implemented, and 3) How the
administrative infrastructure used for information transfer is designed. E-government
affects the business environment directly through this third channel, by simplifying and
automating the administrative infrastructure. As Zuurmond and Robben (2009) specify,
the most important source of administrative burden is paper-based bureaucracy.
Indeed, as it was already recognized by the end of the 80s, computerization lowers
communications time and costs (Hammer and Mangurian 1987; Malone, Yates, and
Benjamin 1987).
Allers (1994) looks at government in this system as a provider of services that
cost money to implement and offer, but create cost savings for businesses. This
essentially means that there is a transfer of costs from business to the state and if we
believe that the market is better at allocating additional resources, such pure cost-
transfer mechanisms already generate efficiency gains. But automating processes and
making them paperless not only transfers costs from private to public sector, but also
reduces costs. Time spent on getting things done, the costs of paper handling, the time
needed to fill out forms, the time of message transportation, and the time needed for
processing incoming data, just to name a few, are either eliminated or drastically
reduced. Unseen it may be, but standardizing reporting enables the creation of
standardized and consolidated databases, that can make administrative processes
even more streamlined in the medium to long run (Bergeron and Raymond 1997;
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Boonstra and de Vries 2005; Elgarah et al. 2005; Henriksen 2002; Mäkipää 2006;
Young, Carr, and Rainer 2000). Furthermore, empirical evidence confirms that the use
of e-government helps founders to establish companies faster and more cheaply, and
boosts company productivity in general (Badri and Alshare 2008). All this shows well
that government has in its discretion a powerful set of tools to unencumber business
and boost growth in the form of electronic service delivery and administration.
E-Government and the Openness of the Country
Why is Openness Important?
The basic economic paradigm of trade is the exploitation of comparative
advantages for the mutual benefit of trading partners by specializing in producing the
good or service with the least opportunity cost, and then trading these. As Krugman
(1979, 1980) explains, gains arise from more variety to consumers and higher quality,
and lower equilibrium prices as firms reap cost-efficiency from economies of scale. As
Andersen and Babula (2008) reports in their review of papers on the subject, the size
and the distribution of welfare gains are disputed by scholars, but not their existence
as such.
The relation of trade and growth is better understood (Andersen and Babula
2008): Trade affects productivity mainly through two channels: The first is that
openness enables quicker capital accumulation leading to higher growth levels. This
was found not to be the strongest channel (Klenow and Rodriguez-Clare 1997; Hall
and Jones 1999). The channel empirically confirmed to be more relevant is a second
one, productivity growth (Frankel and Romer 1999): Exposure to competition by trade
incentivizes firms to be more efficient and wet out inefficient firms, leading to an
average higher productivity; it gives access to foreign intermediary inputs and
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technology; it expands market size enabling economies of scale; and it fosters
international knowledge diffusion. It is empirically measured by Coe and Helpman
(1995) that foreign R&D enhances domestic productivity and further confirmed by
others (Keller 1998; Lumenga-Neso, Olarreaga, and Schiff 2005; Сое, Helpman, and
Hoffmaister 1997). It should also be pointed out that trade can lead to the opposite
effect if a country happens to be specialized on a knowledge-unintensive industry, or
there is a lack of complementary inputs needed for the production of relatively
technologically advanced goods (Basu and Weil 1996; Acemoglu and Zilibotti 2001).
A number of empirical studies have proved these theories: Hakura and Choudhri
(2000) confirmed that openness increases productivity growth, especially through
import competition and especially in mid-growth sectors. They have also found a weak
positive effect through the export expansion channel in the case of high-productivity
sectors. No effect was confirmed for low productivity sectors, as they are probably
engaged in niche-markets or went bankrupt from foreign competition. Paolo Figini and
Enrico Santarelli (2006) and Gozgor (2014) examined the effect trade has on
unemployment in the G7 countries and have found that all trade openness measures
are inversely correlated with unemployment. The study of Ramona and Ioana (2014)
suggest that the flow of FDI, an important component of international flows, is a good
predictor of changes in long-run economic growth (positive relationship). Gokcekus,
Muchova, and Brincikova (2015) discovered that not only technology and knowledge
spills over form more advanced trading partners, but business culture as well: They
have found that openness to a less corrupt trading partner reduces corruption levels of
a country.
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How can E-Government Influence Openness?
As the empirical studies also point out, FDI is a vital aspect of international flows.
A number of factors closely related to competitiveness influence a country's
attractiveness to foreign capital. Schuller (2008) studied the level of globalization and
openness in the Baltic states since the change of systems and he points out that the
Baltic states had increasing levels of openness and competitiveness since the fall of
the Soviet Union, and they maintain these high levels ever since. The capability to
attract FDI is very much linked to competitiveness and as it is discussed in the previous
chapter the e-government is one of the tools that can raise that.
Besides the basic gravity-theory mandating that FDI flows depend on economy
sizes, there are a number of other factors influencing the decision to move capital to a
country. Tintin (2013) investigated the institutional determinants of FDI in post-socialist
economies by including four composite institutional variables alongside traditional
macroeconomic ones: economic freedoms, state fragility, political rights, and civil
liberties, along with GDP and openness measures. The analysis finds all factors
significant and robust in explaining FDI-growth. In connection to e-government, the
variable of interest is economic freedoms, which is a composite index of 10 measures
revolving around rule of law, government size, regulatory efficiency and market
openness. Measures about regulatory efficiency are particularly influenced by e-
government as those are focused on regulatory and administrative burden,
transparency, and the ease of setting up new ventures. Since the study have found
these variables to be robust in explaining changes in FDI, it is plausible that a well-
implemented e-government system can indeed increase the openness of the country.
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ANALYSIS
Usage Data
For the purposes of this study the number of authentications and the number of
signatures is used to measure the usage of digital services.
Authentications happen every time a user logs in to a service, making this an
excellent measure, as it not only shows the number of people using these services,
but also embodies usage frequency, this way being a measure of usage intensity.
The digital signature is a cornerstone to the e-government system, as a digital
signature is needed whenever a paper-based signature would be required if
administration was done on paper. This way it basically shows the number of
transactions, contracts and settled errands via the system.
0
50
100
150
200
250
300
350
400
450
500
20
03
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Mill
ion
s
Cumulative number of Authentications and Signatures
(annual average)
Authentications Signatures
0%
50%
100%
150%
200%
0
20
40
60
80
100
1202
00
3
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Mill
ion
s
Stationarity and Transformations
Auth. chg. Sign. chg.
Auth. %chg. Sign. %chg.
Figure 6 – Usage Data of Digital Services
(Source: Certification Authority)
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Data was sourced from the Certification Authority of the system, handling all
authentications and signatures for all services.1
The provided dataset contains the growth in the number of authentications and
signatures per month from January 2007 to March 2016. This data was transformed to
monthly levels, which in turn was used to create the time series of annual changes and
annual averages as the overwhelming majority of the dependent variables in this
research are only available in the form of annual data.
The Vassil (2015) paper contained annual changes for years 2003-2006, so these
were added to the dataset's annual figures resulting in having annual data 2003-2016
and monthly data 2007/01 - 2016/04.
Citizens’ Attitudes
Hypotheses
Based on the literature review, the experience of using digital services can
strengthen people's trust in the state in a multitude of ways:
Increased perceived performance of government service delivery
(Przeworski et al. 1996)
Increased perception of a transparent and efficient administration
(Mishler and Rose 2001)
Being a more pleasant and convenient interaction with government
(Lipsky 2010; Levi 1996)
1 Special thanks to Kalev Pihl, CEO of the Certification Authority for providing the usage data, to Robert Krimmer and Crystal LaGrone for their generous help in providing information on the system and for connecting me with Mr. Pihl, and to János Kertész for providing the lead to Professor Krimmer.
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Usage of e-government itself is found to balance out the decrease of trust from
using the Internet, therefore it must influence trust in state positively (Im et al.
2014)
Based on these theories they hypotheses to investigate are the following:
H 1.1 Trust towards institutions: The usage intensity of digital services is
positively correlated with the general prevailing trust in the state.
In order to test this hypothesis, I examine the levels of trust in the Estonian society
using data from the European Social Survey. Furthermore, as it was discussed in the
literature review, perceived level of corruption is also related to level of trust, so I also
examine correlation between usage of digital services and perceived levels of
corruption using the Corruption Perceptions Index of Transparency International. Since
the portfolio of digital services also include the possibility to vote online on elections
and trust in the state is expected to influence turnout on votes, I examine election
turnouts as well:
H 1.2 Corruption perception: The usage intensity of digital services is
negatively correlated with the general perception of corruption of the country.
H 1.3 Election turnouts: The possibility of electronic voting increases election
turnout.
Data Sources and Description
Trust data is acquired form the European Social Survey. It is a survey
administered every two years in all EU Member States. The aim is to gauge the
attitudes of society towards social, political and moral issues. There are 7 variables
relating to trust under the political indicators section of the survey:
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1. Trust in the national parliament
2. Trust in the legal system
3. Trust in the police
4. Trust in politicians
5. Trust in political parties
6. Trust in the European Parliament
7. Trust in the United Nations
All these variables are measured on a 0-10 ordinal scale where 0 means the
respondent doesn't trust the institution at all and 10 means the opposite. Data is
available 2002-2012 for every two years for all countries, resulting in 5 data points.
Results of the 7th round are published continuously, and the Estonian data is already
public, but not of all countries. Due to the small number of data points, only descriptive
analysis is performed.
Corruption data is sourced from Transparency International. The Corruption
Perceptions Index (CPI) is a composite index aggregating the assessment of 12
institutions and 13 surveys mainly based on the assessment of analysts and business
people. The survey assigns a score to each country on a scale of 0 – 100 since 2012
(0 – 10 before), where 0 is the most corrupt and 100 is the least corrupt. Countries are
also ranked. Since TI doesn’t provide access to a consolidated database, the
aggregation of survey rounds prepared by the Center for Economic Studies (CES) is
used.
For the purposes of this analysis, recent scores of 0-100 are converted back to
the previously used 0-10 scale dividing by 10. Data is available 1998 – 2015 on an
annual basis.
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Election turnout data is acquired from the International Institute for Democracy
and Electoral Assistance (IDEA). The data contains all local, national and European
Parliament elections since the country's re-independence, the first being the national
election of 1992. The following variables are available:
Whether or not digital voting was allowed (Yes / No)
Type of election (National / Local / European)
Date of election
Size of electorate (number of eligible persons)
Turnout (number of votes as a percentage of eligible voters)
Number of votes
Number of digital votes
(Generated) Number of paper votes (All – digital)
(Generated) Share of paper votes (% of all)
(Generated) Share of e-votes (% of all)
Although only 17 data points (8 for e-voting) in total are available, in addition to
descriptive analysis, a regression of turnout on usage data is also performed to allow
for patterns to emerge if any.
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Trust in Institutions
Figure 7 – Estonians’ Levels of Trust in Different Institutions (Scores)
Looking at the ESS results for trust, it is not really possible to conclude anything
regarding the effect of digital service usage, as data resolution is too low and trust is
likely influenced by a vast number of factors through a multitude of channels. What is
clear from the time series is that most trust variables fluctuate around a mean, except
for trust in foreign institutions (EP and UN) which declined by 2014 relative to other
trust variables, as it can be seen how they leave the seasonal pattern. The cyclical
pattern might be due to elections as well: all local maxima are before national elections
and minima are after (national election years were 2003, 2007, 2011, and 2015).
Looking at the changes of Estonians’ trust over time is more informative if
compared to other countries. Also, global influences, such as the financial crisis of
2008 can be accounted for this way. Unfortunately, such ranking can only be done until
2012 at the moment, as 2014 figures are not available for all countries yet:
3
3.5
4
4.5
5
5.5
6
6.5
2004 2006 2008 2010 2012 2014
Estonians' Levels of Trust in Different Institutions(Respondents' averages)
Trust in country'sparliament
Trust in the legalsystem
Trust in the police
Trust in politicians
Trust in political parties
Trust in the EuropeanParliament
Trust in the UnitedNations
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Figure 8 - Estonians’ Levels of Trust in Different Institutions (Ranking)
Looking at rankings, it can be concluded that there seems to be a decline in trust
compared to other countries, but since the timeframe is short, ranking might also
exhibit cyclicality in the medium-to-long run.
H 1.1 All in all, based on the data available, it is not possible to reject the null
hypotheses, and it cannot be said that the usage of digital services is
accompanied by any increase in trust levels.
However, I believe that the failure to find evidence is merely a result of a lack of
adequate data on the issue. As it was mentioned in the literature review, more scholars
have confirmed the positive relationship in other cases, and I see no reason to believe
that psychological mechanisms of trust are different for Estonians. Further study of the
issue is advised as more data becomes available and / or an in-depth primary research
is feasible.
1112 12
19
15
11
14 14
19
15
89
17 17
14
1213 13
20
16
8
1213
19
1617
2221
27
21
1918 18
22
17
2004 2006 2008 2010 2012
Trust in country'sparliament
Trust in the legalsystem
Trust in the police
Trust in politicians
Trust in politicalparties
Trust in theEuropean Parliament
Trust in the UnitedNations
Estonians' Levels of Trust in Different Institutions(Respondents' averages ranked per country)
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Corruption
Figure 9 – Corruption Perception Score and Rank of Estonia
Examining the score and rank of the country over time, we can see a slow but
steady improvement. Trying to back this up with more exact results, the regressions
yield no significant numbers.
H 1.2 Using the available data, the null hypothesis cannot be rejected, and no
relationship can be concluded between using digital services and the perceived
corruption levels in the country. Further study is advised as data becomes more
abundant.
0
1
2
3
4
5
6
7
8
0
5
10
15
20
25
30
35
Perceived Corruption
Rank Perception
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Election Turnout
Figure 10 – Election Turnouts
Looking at the descriptive graph of election statistics over time, three things can
already be seen:
Electronic votes are replacing ordinary votes at a steady rate.
National elections have the highest turnout rates, followed by local and
European ones.
Since the advent of electronic voting, turnout is on an increasing trend in all
elections types.
In order to back up the visual impressions with something more solid, a
regression was run to see how turnouts are related to the type of election, the
availability of e-votes and the usage intensity of digital services:
0%
20%
40%
60%
80%
100%
Dat
e
19
92
-09
-20
19
93
-10
-17
19
95
-03
-05
19
96
-10
-20
19
99
-03
-07
19
99
-10
-17
20
02
-10
-20
20
03
-03
-02
20
04
-06
-13
20
05
-10
-16
20
07
-03
-04
20
09
-06
-07
20
09
-10
-18
20
11
-03
-06
20
13
-10
-20
20
14
-05
-25
Turnout (Local) Turnout (EP)
Turnout (National) Share of paper votes
Share of e-votes
Election turnouts, paper and e-votes
ELECTRONICVOTINGAVAILABLE
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Turnout Turnout
% Change Number of Authentications
-0.000105
(0.0105)
% Change Number of Signatures -0.00396
(0.0134)
Election is Local 0.150 0.147
(0.0663) (0.0688)
Election is National 0.230** 0.231**
(0.0403) (0.0444)
E-vote available 0.132* 0.131*
(0.0394) (0.0441)
Constant 0.270*** 0.276***
(0.0160) (0.0219)
N 9 9
R-sq 0.907 0.908
Standard errors in parentheses * p<0.05 ** p<0.01 *** p<0.001
Table 1 – Regression Results of Election Turnouts and Service Usage
Although having 9 observations is far from solid evidence, the regression backs
up the visual intuition of the descriptive graph: According to the data, overall on
average, the availability of electronic voting is associated with a 9 ~ 17
percentage points higher turnout at an election, controlling for election type.
The regression also shows that election type is a significant to turnout: Local and
European Parliament elections are both exhibiting lower turnout rates, while overall on
average according to the data, a national election is expected have a
19 ~ 27 percentage points higher turnout then municipal or European ones,
controlling for electronic voting availability.
H 1.3 Data seems to confirm the hypothesis that the availability of e-voting
increases election turnouts, however the low number of years available to study
and the lack of an instrumental variable or a control group means that the results
must be taken with a grain of salt, and although causality is intuitively plausible,
formally is not proven by this regression.
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Further validation of the results is advised as more data becomes available over
time.
Competitiveness and Productivity
Hypotheses
As Allers (1994), A. F. M. Nijsen (2003), and Ntaliani, Costopoulou, and Sideridis
(2012) all point out, coping with administrative burden ties down a considerable amount
of company resources. The amount of administrative burden is an integral factor to the
competitiveness of an economy. The advent of the Estonian e-government services
lowers exactly this burden, therefore
H 2.1 It is expected that usage of digital services is correlated with the
improvement of the competitiveness of the economy through reduced regulatory
burden.
In order to test this, I examine scores and ranks of the different aspects of the
Estonian business environment over time, and study the change in different measures
of productivity.
H 2.2 Increased usage intensity of digital services is accompanied by an
increase in productivity.
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Data Sources and Description
Two datasets are used to study the changes of the business environment over
time: The dataset of the Global Competitiveness Report of the World Economic Forum
(WEF GCR) and the Doing Business Report of the World Bank (WB DB).
WEF Global Competitiveness Report. WEF “defines competitiveness as the
set of institutions, policies and factors that determine the level of productivity of an
economy, which in turn sets the level of prosperity that the country can achieve” (World
Economic Forum 2016a). The GCR is published by the WEF every year, and each
country is assessed on the above through a large number of variables having a score
and a country rank for each. Data is available on an annual basis from 2007 to 2016
for the majority of variables. Only variables that have a significant relationship with
service usage are presented in the next section.
WB Doing Business. The WB also prepares a similar report each year, but with
a much narrower focus. While the GCR measures things such as the quality of the
education system, public health or broadband internet availability, the DB report
focuses purely on business related issues, such as time and number of filled out forms
needed to register a business, quality of dispute settlement or protection of minority
investors. Countries are assessed by scores and ranks, but WB also includes a handy
distance to frontier (DTF) metric that takes the best performing country’s score as
100% and expresses other countries score as a percentage of that. DTF values are
available 2004 – 2016 or 2006 – 2016 on an annual basis.
For testing hypothesis 2.1, select business environment variables from the GCR
and DTF values from the DB are regressed on usage data.
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Productivity data is sourced from the latest Penn World Table (PWT) and The
Conference Board Total Economy Database (TED). The following variables are
selected from these databases:
Total Factor Productivity Level
(Current PPPs, USA=1, from PWT, available annually 1990-2011)
Total Factor Productivity Growth
(Tornqvist Index, from PWT, available annually 1993-2014)
Labor Productivity per Hour Worked
(in 2014 USD converted to 2014 price level with updated 2011 PPPs, from TED,
available annually 1990-2015)
Labor Productivity per Hour Worked Growth
(percent change from previous year, from TED, available annually 1990-2015)
Competitiveness
In order to uncover relationships between changes in business environment
scores, an initial subset of 61 variables were selected based on the likelihood of a
relationship suggested by the literature. After the first differenced and log-differenced
transformations of these variables were generated, those level and first-differenced
variables that were non-stationary according to a Philips-Perron and Augmented
Dickey-Fuller tests were dropped in order to minimize the chance of detecting spurious
correlations. The remaining variables were regressed on usage intensity, and after
dropping borderline significant cases, only the variables of regulatory burden and the
aggregate GCI-value remained as significant.
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Dependent Variables Explanatory Variables Reg. Sats
Name Type, improve worsen
ß of Number of
Sign. ∆%
ß of Number of Auth. ∆%
Std. Err. Of N. Sign ∆%
Std. Err. Of N. Auth ∆%
N R2
Burden of government regulation, 1-7 (best)
Score ∆ 0.175* (0.0575) 9 0.513
Score ∆ 0.235** (0.0601) 9 0.585
Score ∆% 0.0411* (0.0135) 9 0.511
Score ∆% 0.0550** (0.0143) 9 0.577
GCI Aggregate Score 0.0521** (0.0112) 10 0.469
Score 0.0462*** (0.00874) 10 0.445
Rank -2.051** (0.439) 10 0.492
Rank -1.719*** (0.325) 10 0.417
Score ∆% -0.0178** (0.00472) 9 0.492
Score ∆% -0.0244*** (0.00381) 9 0.588
Score ∆ -0.0841** (0.0218) 9 0.505
Score ∆ -0.115*** (0.0175) 9 0.600
Table 2 – Regression Results of Business Environment Variables and Service Usage
Regulatory burden. Although the score of regulatory burden were decreasing
and the corresponding rank worsening since 2010, the regression results suggest that
the decline would have been even worse without digital services, as there is a
significant positive correlation between the change in service usage and change in
score.
Aggregate GCI Score. The aggregate GCI score is the weighted score resulting
from aggregate scores of subcategories of variables, therefore all GCI variables
influence this figure. The GCI scores of Estonia are improving, just like its ranking.
According to the regression, both the scores and ranks are improving with service
usage, but results also show that the higher the increase in service usage, the lower
the improvement in GCI score. This has probably to do with changes in other variables
making up this aggregate index and the small number of observations.
H 2.1 Based on the results the hypothesis can be confirmed. Usage of digital
services indeed seem to be associated with improvement in the change of
regulatory burden scores, but the scores merely decrease less instead of
increasing, suggesting that some other factors are offsetting a positive effect of
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digital services, if any. Competitiveness scores are recovering since 2010, but
in order to establish causality, a more in-depth analysis needs to be conducted.
Productivity
The regression results on productivity show no connection between usage and
productivity variables whatsoever.
H 2.2 There is not enough evidence to reject the null hypothesis, and it cannot
be concluded that an increase in the usage of digital services is associated with
any change in productivity levels.
Openness
Hypotheses
As (Schuller 2008) pointed out in his study, the Baltic states had increasing levels
of openness and competitiveness since the change of systems, and as Tintin (2013)
states, regulatory efficiency is an important determinant of the competiveness and
therefore attractiveness of a country. In this section, I examine if there is a relationship
between the usage intensity of digital services and foreign direct investment inflows to
the country.
H 3.1 An increase in the usage intensity of digital services is associated with
an increase of FDI-inflows into the country.
If the country indeed becomes a more attractive investment destination, that
should also show up in business demographics data. Therefore, the second hypothesis
for this chapter is:
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H 3.2 An increase in the usage intensity of digital services is associated with
an increase of foreign business ownership in the country.
Data Sources and Description
Foreign Direct Investment was the sole data in this paper that is available in a
higher resolution than annual. Quarterly data was acquired from the Federal Reserve
Bank of St. Louis and Eurostat:
FDI stocks
(National currency, available 1992Q2 – 2013Q4 quarterly, FED)
FDI flows
(Percentage change quarter-on-quarter, available 1992Q3 – 2013Q4 quarterly,
FED)
FDI Stocks
(mEUR, 1997-2012, annual, Eurostat)
Business demography data was sourced from the Estonian Statistical Office
and Eurostat.
Number of enterprises by ownership
(count, available 2000-2015 annually, Statistics Estonia)
Number of enterprise groups by country of owner
(count, available 2004-2015, Statistics Estonia)
Enterprise birth rates
(New companies / All, %, 2004-2013, Statistics Estonia)
Market integration
(Average values of inward and outward FDI / GDP * 1000, 1997-2012, Eurostat)
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FDI
After transforming monthly usage data to quarterly and regressing FDI growth,
regressions were performed using various lag structures. Probably due to the still small
number of observations, some lag specifications showed borderline significant
coefficients, but those were not robust. Therefore, the most conservative result is
reported here, which is:
FDI (Kroon) Change in FDI (mEUR)
% Chg. In Number of Signatures
135,777,930.0** 312.9*
(36,166,744.7) (112.5)
Constant 238,040,173.4*** 637.5 (39,991,818.3) (329.2) N 24 9 R-sq 0.085 0.190
Table 3 – Regression Results of FDI Stocks and Service Usage
A quarter with 10 percentage points higher increase in the usage intensity shown
by the number of signatures is associated by a 13 million kroons (~ 870 thousand €)
higher FDI stock in the country, overall on average, according to the limited data
available.
H 3.1 Based on the limited data available the hypothesis can be confirmed,
although a more in-depth study should be undertaken to establish the existence
and dynamics of causality between the usage of digital services and FDI-levels.
Business Demography
Examining changes in business demography in association with changes in the
usage intensity of digital services yields no significant results.
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H 3.2 Using available data, it is not possible to reject the null hypothesis, and I
cannot conclude that there is any association between digital service usage
intensity and changes in business demography in terms of foreign ownership.
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CONCLUSIONS
The goal of this thesis was to establish if there is a positive association between
the usage of e-government services and citizen’s attitudes, competitiveness and
productivity, and openness of the economy in the case of Estonia.
In the area of citizens’ attitudes, although literature suggests, nor changes in trust
levels, nor changes in the perception of corruption could be verified to have a
relationship with the changes in usage intensity. However, it seems that the availability
of electronic voting is associated with increased election turnouts.
In terms of competitiveness and productivity, changes in productivity could not be
confirmed to have any relationship with changes in service usage, but in terms of the
business environment, higher usage is associated with a lower deterioration of the
administrative burden scores of the country.
When it comes to the openness of the country, it cannot be concluded that foreign
ownership is changing significantly in terms of the usage of digital services, but there
seems to be a positive relationship with the stock of FDI.
Based on these findings, it can be concluded that the availability and the usage
intensity of digital services is associated with favorable tendencies in the economy, but
those favorable changes are in some cases also being offset by negative ones (e.g.
regulatory burden), or diminish on a macro level (e.g. productivity).
Overall, the goal of the thesis to measure changes in such complex and rather
qualitative phenomena as trust, or such aggregate macro-level measures as
productivity was probably too ambitious for the timeframe examined, as all variables
involved in this analysis except for FDI were available only on an annual basis resulting
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in a major bottleneck for this project. Despite this shortage of data, some findings are
still visible, but in order to establish the existence and dynamics of causal relationships,
conducting further, more in-depth research is advised.
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POLICY RECOMMENDATIONS
Based on the conclusions of this research, it seems evident that the digital
services offered in Estonia are associated with favorable tendencies in election
turnouts, regulatory burden and FDI-levels, therefore these should be continued and it
is recommended to further extend the scope of these services.
However, it is suspected that the favorable effects of the availability and spread
of digital service usage cannot fully manifest at this moment, as regulatory burden
scores are still worsening for some reason, and productivity is below optimal levels.
Unfortunately, the score of regulatory burden in the Global Competitiveness
Report is based on an expert survey that is publicly unavailable, so the exact cause of
the worsening scores are not known. Nevertheless, competitiveness and productivity
can be improved by tackling some other areas of the economy. As the overall
assessment of Estonian competitiveness was out of the scope of this paper, the
following recommendations are partly based on the assessment of international
organizations:
According to the WB DB Report, Estonia has worsening rankings in the areas of
Getting Credit, Enforcing Contracts and Resolving Insolvency (World Bank
2016). Therefore, it is recommended to improve regulation in these areas by
developing reforms based on consultation with businesses and considering the
adoption of best practices from other countries.
Based on the most recent country report of the European Commission, it is
further recommended to close the gender gap in educational participation, and
combat early school leaving. The country is currently facing skill shortages in
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developing technology-intensive high value-added sectors, and this should be
mitigated through investing in education (European Commission 2016).
Energy efficiency is still lower than in other EU countries, therefore investments
should be made into refurbishing public buildings and a scheme should be set
up to support the energy efficiency upgrades of residential buildings (European
Commission 2016).
All-in-all, things in the country look good: The aggregate score for the business
environment is improving in both benchmarks, the country's finances are in
exceptionally good shape, and employment is at record levels (European Commission
2016). This way, the most important recommendation is to keep up the current
progress, carry on with ongoing reforms while maintaining their political support
in the population, by ensuring that benefits reach all parts of society and are well
communicated.
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